Enterprise-Compatible Deployment Architecture
Our systems integrate with existing operational infrastructure without requiring camera replacement or significant workflow disruption. Deployment models are designed around your facility's existing technical environment.
Works With Your Existing Infrastructure
Our systems integrate with existing operational infrastructure including RTSP camera environments, ONVIF-compatible systems, VMS platforms, local edge compute infrastructure, secure cloud orchestration layers, and facility alerting workflows.
Three Deployment Models
Fully On-Premise
All inference and data processing occurs within the facility network. No video data leaves the building. Suitable for facilities with strict data residency requirements or limited internet connectivity.
Hybrid Operational
Edge inference on-site with optional cloud orchestration for reporting, remote monitoring, and multi-facility coordination. Balances data control with operational flexibility.
Managed Infrastructure
Fully managed deployment including hardware provisioning, software maintenance, monitoring, and support. Suitable for facilities without dedicated IT infrastructure teams.
Inference Runs on Your Infrastructure
Every inference pipeline — pose estimation, fall detection, resident identification, and movement tracking — executes entirely on hardware within your facility. No video frames, skeleton data, or identity vectors are transmitted to external servers. Privacy is enforced at the infrastructure level, not by policy.
Our models are purpose-built for the low-light, wide-angle, and partially-occluded conditions typical of care facility camera deployments — not adapted from general-purpose datasets.
Pose Estimation
Skeletal Keypoint Tracking
17-point body keypoint detection at 15+ fps per camera stream. Tracks joint angles, center-of-mass trajectory, and limb velocity vectors in real time.
Fall Detection
Biomechanical Fall Classification
Multi-frame kinematic analysis distinguishing controlled sit-down events from uncontrolled falls. Trained on care-facility-specific fall patterns including slow-collapse and lateral fall types.
Facial Recognition
Resident Identification
On-device face embedding and matching against a facility-local identity database. Resident ID vectors never leave the facility network. Supports re-identification across non-overlapping camera zones.
Multi-Object Tracking
Persistent Identity Tracking
Continuous resident and staff trajectory tracking across camera handoffs using appearance and motion cues. Maintains persistent track IDs through occlusion and re-entry events.
System Requirements and Capabilities
Camera Capacity
4 – 128+
cameras per deployment unit
Latency
< 2 seconds
event detection to alert delivery
Data Retention
Configurable
30 – 365 days, on-premise or cloud
Uptime
99.5%+
target operational availability